How to Strengthen Ad Mail by Building a Digital Information Market
Bibliographic record
Abstract
The US Postal Service suffered a $5B loss in FY 2013 following on a $15.9B loss the year before. One of the challenges has been the diversion of revenues from physical ad mail to online channels such as Google and Facebook. This paper articulates the design of an information market that could help physical ads compete with digital ads. The proposed mechanism offers a closed loop information feedback with attractive properties. (i) Recipients can declare what mail they do not want and become protected thereafter. (ii) Advertisers may learn what recipients do want. (iii) Recipients are rewarded for data they volunteer. (iv) The mechanism provides a simple means to convert offline contact to online purchase. (v) A data layer facilitates business partnership with USPS in order to offer new services. (vi) A new type of stamp provides USPS with a new source of revenue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.033 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".